Mechanical design and trajectory planning of a lower limb rehabilitation robot with a variable workspace

Wang, Hongbo; Yan, Feng; Yu, Hongnian; Wang, Zhenghui; Vladareanuv, Victor; Du, Yaxin

doi:10.1177/1729881418776855

Cited by 12 publications

(10 citation statements)

References 34 publications

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“…The lower limb training with sitting and lying posture can reduce the weight burden of the user’s buttocks and legs, and increase the range of motion of the lower limb joints. 54 Cheng et al 55 have designed a sitting and lying exoskeleton robot for lower limb rehabilitation, as shown in Figure 12, which can be used to improve the stability of patient rehabilitation training. The sitting and lying rehabilitation robot designed by Chen et al, 56 as shown in Figure 13, can realize adaptive and compliant control of reference trajectory.…”

Section: Resultsmentioning

confidence: 99%

Lower limb rehabilitation exoskeleton robot: A review

Zhou

Yang

Xue

2021

Advances in Mechanical Engineering

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Lower limb rehabilitation exoskeleton robots (LLRERs) play a positive role in lower limb rehabilitation and assistance for patients with lower limb disorders, and they are helpful to improve patients’ physical status. More and more experiments pay more attention to the kinematic and dynamic data characteristics of different patient groups. However, it is not clear whether these devices have broad adaptability and their clinical significance, so it is necessary to summarize and analyze these research results. This paper summarizes the LLRERs prototype and product in recent years, also compares the advantages and disadvantages of the theory and technology used in these research, and compares the functional characteristics of the devices, finally summarizes the aspects of the LLRERs to be improved. These devices apply advanced theories, techniques or structures, as well as human kinematics and dynamics data. However, due to the complexity of human body characteristics and movement rules, the theory or technology applied in the study design of LLRERs remains to be further studied, which can be improved in many aspects, such as improve the human-computer cooperation of equipment or carry out clinical trials. This paper can provide reference for researchers and designers in the future study, as well as understanding and selecting LLRERs for all kinds of therapist and patients.

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Section: Resultsmentioning

confidence: 99%

Lower limb rehabilitation exoskeleton robot: A review

Zhou

Yang

Xue

2021

Advances in Mechanical Engineering

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“…The sitting-lying type LLRTS, such as MotionMaker, can let patients sit on a chair and adopt different training modes at a patient's different rehabilitation stages. The length of the mechanical leg and the width between the two legs can be adjusted to adapt to patients with different heights and shapes [24][25][26]. The multi-posture LLRTS combines the advantages of the sitting-lying type and the standing type LLRTS, and can realize the posture transformation from the lying to standing posture.…”

Section: Stroke Patientmentioning

confidence: 99%

Design and Analysis of a Lower Limb Rehabilitation Training Component for Bedridden Stroke Patients

et al. 2021

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Carrying out the immediate rehabilitation interventional therapy will better improve the curative effect of rehabilitation therapy, after the condition of bedridden stroke patients becomes stable. A new lower limb rehabilitation training module, as a component of a synchronous rehabilitation robot for bedridden stroke patients’ upper and lower limbs, is proposed. It can electrically adjust the body shape of patients with a different weight and height. Firstly, the innovative mechanism design of the lower limb rehabilitation training module is studied. Then, the mechanism of the lower limb rehabilitation module is simplified and the geometric relationship of the human–machine linkage mechanism is deduced. Next, the trajectory planning and dynamic modeling of the human–machine linkage mechanism are carried out. Based on the analysis of the static moment safety protection of the human–machine linkage model, the motor driving force required in the rehabilitation process is calculated to achieve the purpose of rationalizing the rehabilitation movement of the patient’s lower limb. To reconstruct the patient’s motor functions, an active training control strategy based on the sandy soil model is proposed. Finally, the experimental platform of the proposed robot is constructed, and the preliminary physical experiment proves the feasibility of the lower limb rehabilitation component.

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“…The joint no-load moment in LLR-II man–machine coupled motion is affected by the weight of the mechanical leg and the patient’s leg, and it can be expressed as a nonlinear function of joint variables [41]. Mbold-italicn×1=bold-italicFfalse(θbold-italicn×1false) where, Mbold-italicn×1 is the column vector of joint no-load torque, bold-italicF(•) is the mapping function, and θbold-italicn×1 is the joint variable.…”

Section: Llr-ii Rehabilitation Robotmentioning

confidence: 99%

Detection of Participation and Training Task Difficulty Applied to the Multi-Sensor Systems of Rehabilitation Robots

Yan

Wang

Vlădăreanu

et al. 2019

Sensors

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In the process of rehabilitation training for stroke patients, the rehabilitation effect is positively affected by how much physical activity the patients take part in. Most of the signals used to measure the patients’ participation are EMG signals or oxygen consumption, which increase the cost and the complexity of the robotic device. In this work, we design a multi-sensor system robot with torque and six-dimensional force sensors to gauge the patients’ participation in training. By establishing the static equation of the mechanical leg, the man–machine interaction force of the patient can be accurately extracted. Using the impedance model, the auxiliary force training mode is established, and the difficulty of the target task is changed by adjusting the K value of auxiliary force. Participation models with three intensities were developed offline using support vector machines, for which the C and σ parameters are optimized by the hybrid quantum particle swarm optimization and support vector machines (Hybrid QPSO-SVM) algorithm. An experimental statistical analysis was conducted on ten volunteers’ motion representation in different training tasks, which are divided into three stages: over-challenge, challenge, less challenge, by choosing characteristic quantities with significant differences among the various difficulty task stages, as a training set for the support vector machines (SVM). Experimental results from 12 volunteers, with tasks conducted on the lower limb rehabilitation robot LLR-II show that the rehabilitation robot can accurately predict patient participation and training task difficulty. The prediction accuracy reflects the superiority of the Hybrid QPSO-SVM algorithm.

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Mechanical design and trajectory planning of a lower limb rehabilitation robot with a variable workspace

Cited by 12 publications

References 34 publications

Lower limb rehabilitation exoskeleton robot: A review

Lower limb rehabilitation exoskeleton robot: A review

Design and Analysis of a Lower Limb Rehabilitation Training Component for Bedridden Stroke Patients

Detection of Participation and Training Task Difficulty Applied to the Multi-Sensor Systems of Rehabilitation Robots

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